Identifying genes associated with invasive disease in S. pneumoniae by applying a machine learning approach to whole genome sequence typing data.

ABSTRACT: Streptococcus pneumoniae, a normal commensal of the upper respiratory tract, is a major public health concern, responsible for substantial global morbidity and mortality due to pneumonia, meningitis and sepsis. Why some pneumococci invade the bloodstream or CSF (so-called invasive pneumococcal disease; IPD) is uncertain. In this study we identify genes associated with IPD. We transform whole genome sequence (WGS) data into a sequence typing scheme, while avoiding the caveat of using an arbitrary genome as a reference by substituting it with a constructed pangenome. We then employ a random forest machine-learning algorithm on the transformed data, and find 43 genes consistently associated with IPD across three geographically distinct WGS data sets of pneumococcal carriage isolates. Of the g

SUBMITTER: Obolski U 

PROVIDER: S-EPMC6411942 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature